@article{4491,
  author = {Yiwei Huang, Jingteng Chen, Xianghan Zheng, Weipeng Xie, Huan Wang, Zhibin Xie,  Yunliang Li},
  title = {Distribution Network External Force Damage Warning System under Cloud-Edge Collaborative Architecture},
  journal = {Journal of Networking Technology},
  year = {2025},
  volume = {16},
  number = {2},
  doi = {https://doi.org/10.6025/jnt/2025/16/2/51-61},
  url = {https://www.dline.info/jnt/fulltext/v16n2/jntv16n2_2.pdf},
  abstract = {This paper designs a distribution network external force damage early warning system based on audio
classification technology, focusing on the potential damage that large construction machinery might cause
to cables. By utilizing audio data, the system can accurately monitor and analyze the real-time status of
construction sites to identify potential cable damage activities. Leveraging a cloud-edge collaboration architecture,
the system employs cloud-based model training and edge computing technology to achieve realtime
monitoring and data processing, thereby enhancing the accuracy and timeliness of warnings. Integrating
machine learning algorithms and pattern recognition technology, the system can automatically analyze
and predict the impact of construction machinery on cables. Through a visualization system, it provides
real-time warnings and response measures, effectively reducing losses caused by external damage and
ensuring the safe operation and reliability of the distribution network and power supply.},
}